Abstract

In the present work, the roles of inorganic oxide nanoparticles on the extraction efficiency of polyethylene terephthalate-based nanocomposites were extensively studied. Four fiber coatings based on polyethylene terephthalate nanocomposites containing different types of nanoparticles along with a pristine polyethylene terephthalate polymer were conveniently electrospun on stainless steel wires. The applicability of new fiber coatings were examined by headspace-solid phase microextraction of some environmentally important volatile organic compound such as benzene, toluene, ethylbenzene and xylene (BTEX), as model compounds, from aqueous samples. Subsequently, the extracted analytes were transferred into a gas chromatography by thermal desorption. Parameters affecting the morphology and capability of the prepared nanocomposites including the type of nanoparticles and their doping levels along with the coating time were optimized. Four types of nanoparticles including Fe3O4, SiO2, CoO and NiO were examined as the doping agents and among them the presence of SiO2 in the prepared nanocomposite was prominent. The homogeneity and the porous surface structure of the SiO2-polyethylene terephthalate nanocomposite were confirmed by scanning electron microscopy indicating that the nanofibers diameters were lower than 300nm. In addition, important parameters influencing the extraction and desorption process such as temperature and extraction time, ionic strength and desorption conditions were optimized. Eventually, the developed method was validated by gas chromatography–mass spectrometry. Under optimized conditions, the relative standard deviation values for a double distilled water spiked with the selected volatile organic compounds at 50ngL−1 were 2–7% (n=3) while the limits of detection were between 0.7 and 0.9ngL−1. The method was linear in the concentration range of 10 to 1000ngL−1 (R2>0.9992). Finally, the developed method was applied to the analysis of Kalan dam and tap water samples and the relative recovery values were found to be in the range of 86–102%.

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